CN110941759A - Microblog emotion analysis method - Google Patents

Microblog emotion analysis method Download PDF

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CN110941759A
CN110941759A CN201911142590.0A CN201911142590A CN110941759A CN 110941759 A CN110941759 A CN 110941759A CN 201911142590 A CN201911142590 A CN 201911142590A CN 110941759 A CN110941759 A CN 110941759A
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CN110941759B (en
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张国威
胡汤磊
杨杰
诸葛瑜亮
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Guoyuan Securities Co Ltd
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Abstract

The invention discloses a microblog emotion analysis method which comprises the steps of crawling microblog data and calculating a microblog emotion value; performing label classification on microblogs related to stock market; performing category prediction on the obtained new microblog data by using a random forest model of Smote; and classifying the emotion of each microblog by using a Smote-based random forest algorithm. When the microblog emotion is analyzed, when the negative emotion is relatively high, the microblog emotion expression is more consistent with the trend of the large dish when the large dish (the upper syndrome index) shows a certain degree of decline, and the trend of the large dish can also be shown through emotion fluctuation. The microblog looks empty more, and can be used as a service project and provided for a client to make a decision reference. The stock market peripheral data analysis plays an important role in researching the interaction relationship between the media and the stock market, helping investors to remove the speculative psychology and establishing investment ideas.

Description

Microblog emotion analysis method
Technical Field
The invention relates to a method for analyzing the influence of social media on stock market, in particular to a microblog emotion analysis method.
Background
In the mainstream network social platform of the web2.0 era, microblog is one of the favorite social tools of the majority of internet users. In the information era with speed and efficiency as standards, microblogs not only provide an instant messaging interactive platform spanning time and distance for network users: meanwhile, the microblog also provides a dynamic display platform for expressing self, emotion and personality for the network user. The microblog messages are updated constantly at every moment, and the microblog texts with the emotional information are countless, so that the research on the emotional analysis of the microblog becomes possible.
Compared with a blog, the content of the microblog is only composed of simple single-language phrases, the arrangement organization of the language has no blog data specification, the word number is limited within 140, and accurate information is more difficult to extract; the microblog is characterized in that: the information is released quickly, the information transmission speed is high, attention and mutual attention behaviors exist, and the judgment of the influence needs to be explored; moreover, the microblog content has certain randomness, the emotional expression is direct, but every microblog published by a concerned big V is related to a stock market, and screening is needed according to keywords such as stock market, stock, securities, sun line and the like.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: how to conduct effective investment guidance according to microblog data provides a microblog emotion analysis method.
The invention solves the technical problems through the following technical scheme, and the invention comprises the following steps:
(1) crawling microblog data and calculating a microblog emotion value;
(2) performing label classification on microblogs related to stock market;
(3) performing category prediction on the obtained new microblog data by using a random forest model of Smote;
(4) the sentiment classification of each microblog by using a random forest algorithm based on Smote is as follows:
(41) for the jth microblog, the forwarding amount is assumed to be rjAnd the evaluation quantity is cjThe amount of praise is zjDefining the heat omega of the microbumpjThe calculation formula is as follows:
Figure BDA0002281362590000011
(42) if the ith large V publishes the micro blogs related to the stock market in a period of timeiAnd if so, the three emotions transmitted by the large V through the published microblog in the period are respectively as follows:
Figure BDA0002281362590000021
Figure BDA0002281362590000022
Figure BDA0002281362590000023
the probability of three categories of negative emotion, no emotion and positive emotion is p-1j、p0j、p1j；
(43) Noting that the number of people concerned by the ith big V in the period is focusiThe number of vermicelli is fansiThe total number of microblog sending is weiboi， c1、c2For a given constant, the influence of large V is evaluated
Figure BDA0002281362590000024
When, the definition is as follows:
Figure BDA0002281362590000025
Figure BDA0002281362590000026
Fansi＝fansi
then
Figure BDA00022813625900000210
(44) The overall emotion indexes of n large V on the microblog platform are as follows:
negative emotion index Qbad：
Figure BDA0002281362590000027
Non-emotional index Qave：
Figure BDA0002281362590000028
Positive emotion index Qgood：
Figure BDA0002281362590000029
The step (1) comprises the following steps:
(11) establishing an emotion dictionary, including a positive and negative dictionary, a degree word dictionary, a negative word dictionary and an expression word dictionary;
(12) calculating a microblog emotion value: weight S corresponding to the active negative emotion word S is assumediThe weight corresponding to the emoticon SS is SSiEach microblog is subjected to word segmentation, cleaning and the like to obtain m1Positive and negative emotion words, m2Different expression words are arranged, and the number of the ith expression word is ni，
If m (less than or equal to m) exists1) The positive negative emotion words are only preceded by the weight WiIf the degree word is modified, the emotional value O of the microblog iskComprises the following steps:
Figure BDA0002281362590000031
if m (less than or equal to m) exists1) Before the positive and negative emotion wordsOnly the occurrence weight is Wi noIf the negative word is modified, the emotional value O of the microblog iskComprises the following steps:
Figure BDA0002281362590000032
if m (less than or equal to m) exists1) The positive negative emotion words are preceded by the existing weight WiIs modified by the term of degree, and has a weight of Wi noIf the negative word is modified, the emotional value O of the microblog iskComprises the following steps:
Figure BDA0002281362590000033
in the step (2), the microblogs related to the stock market are manually classified into three categories:
the first type is microblogs expressing positive emotions to the stock market, the emotion tag is set to be 1, the second type is microblogs expressing negative emotions to the stock market, the emotion tag is set to be-1, the third type is microblogs not expressing emotion viewpoints, and the emotion tag is set to be 0.
Selecting a random forest algorithm based on Smote to classify original samples, wherein independent variables of training samples in a classifier are acquired based on the method for calculating the emotion value according to the weight degree designed by an emotion analysis dictionary, namely: and calculating scores for different types and different weights of words respectively.
Extracting 12-dimensional attributes of each microblog according to the scores, wherein the positive emotion is divided into 3 dimensions, the negative emotion is divided into 3 dimensions, the degree words are divided into 4 dimensions, the negative words are 1 dimension, and the emotion value is 1 dimension.
Compared with the prior art, the invention has the following advantages: when the microblog emotion is analyzed, when the negative emotion is relatively high, the microblog emotion expression is more consistent with the trend of the large dish when the large dish (the upper syndrome index) shows a certain degree of decline, and the trend of the large dish can also be shown through emotion fluctuation. The microblog is empty, and can be used as a service project and provided for a client to make decision reference. The stock market peripheral data analysis plays an important role in researching the interaction relationship between the media and the stock market, helping investors to remove the investment psychology and establishing the investment concept.
Drawings
FIG. 1 is a partial weighted word from an emotion dictionary of the present invention;
FIG. 2 is an emotional tag diagram of a portion of microblog data;
FIG. 3 is a Smote-based random forest model training result;
FIG. 4 is a diagram of overall emotional trends of a microblog;
fig. 5 is a plot of the large-plate (upper syndrome index) market K.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
In this embodiment, the microblog data is first crawled, and the main acquirable information includes: publishing time, nickname, microblog id number, forwarding number, comment number, praise number, microblog content, grade of microblog comment person, authentication condition, fan number, gender, address and the like. A large number of microblogs are obtained by setting whether attention is paid to the number of the famous large evaluation V and the interval time, and the attention large V is mainly screened according to the fan amount.
Microblog emotion analysis mainly researches positive or negative emotions of a large stock V in a period of time, and can objectively reflect investment confidence of investors in a stock market to some extent. The essential link for emotion analysis is to establish an emotion dictionary with weight, perform word segmentation, cleaning, emotion dictionary matching and the like on each microblog, and calculate an emotion value so as to measure the important indexes of microblog emotion.
The built emotion dictionary only comprises: the positive and negative dictionary txt, the degree word dictionary txt and the negative word dictionary txt, considering the wide use of the emoticons, the expressive words are also added into the dictionary emotion dictionary and are endowed with corresponding weights, and partial weighted words are shown in the figure 1.
The microblog sentiment value calculating method comprises the following steps: weight S corresponding to the active negative emotion word S is assumediThe weight corresponding to the emoticon SS is SSiEach microblog is subjected to word segmentation, cleaning and the like to obtain m1Positive and negative emotion words, m2Different expression words are arranged, and the number of the ith expression word is ni。
If m (less than or equal to m) exists1) The positive negative emotion words are only preceded by the weight WiIf the degree word is modified, the emotional value O of the microblog iskComprises the following steps:
Figure BDA0002281362590000041
if m (less than or equal to m) exists1) The positive negative emotion words are only preceded by the weight Wi noIf the negative word is modified, the emotional value O of the microblog iskComprises the following steps:
Figure BDA0002281362590000051
if m (less than or equal to m) exists1) The positive negative emotion words are preceded by the existing weight WiIs modified by the term of degree, and has a weight of Wi noIf the negative word is modified, the emotional value O of the microblog iskComprises the following steps:
Figure BDA0002281362590000052
in this embodiment, 837 microblogs are manually marked and preliminarily counted, and the microblogs related to the stock market account for 1/2 of the total number of microblogs, which are divided into three categories: the first type is microblogs expressing positive emotions to stock market, the emotion tag is set to be 1, the second type is microblogs expressing negative emotions to stock market, the emotion tag is set to be-1, the third type is microblogs not expressing emotion viewpoints, the emotion tag is set to be 0, and fig. 2 is an emotion tag diagram of partial microblog data.
By the microblog emotion value calculation method, the emotion value of each microblog related to the stock market is calculated, considering that certain deviation possibly exists in microblog classification according to a given emotion threshold value and the classification accuracy is directly influenced, a logistic regression, a random forest, a deep neural network and the like are tried to be used as classifiers for carrying out emotion analysis on the microblog, due to the fact that the original sample data are unbalanced, in order to guarantee the classification effect of the classifiers, the original samples are balanced by using a Smote algorithm, and the random forest algorithm based on Smote is finally selected for classification after the classification effect of each classifier is compared.
The independent variables of the training samples in the classifier are acquired based on the rule for calculating the emotion value according to the weight degree designed by the emotion analysis dictionary, namely: and calculating scores for different types and different weights of words respectively. Extracting 12-dimensional attributes of each microblog according to the scores, wherein the positive emotion is divided into 3 dimensions, the negative emotion is divided into 3 dimensions, the degree words are divided into 4 dimensions, the negative words are 1 dimension, and the emotion value is 1 dimension.
Training microblog data with labels by using a Smote-based random forest model, training 4/5 samples and testing 1/5 samples, wherein preliminary results are shown in FIG. 3.
From the viewpoint of the overall accuracy and various category indexes, the random forest model of Smote can be directly used for predicting new microblog data, and the training model is used for performing category prediction on the new microblog data acquired every day.
The random forest algorithm based on Smote is used for realizing emotion classification of each microblog, and when the jth microblog is classified, three types of probabilities of negative emotion, no emotion and positive emotion are respectively p-1j、p0j、p1jAnd judging the overall emotional condition on the microblog platform according to the judgment result, wherein the heat degree omega of each microblog is consideredjThe influence of different large V also needs to be considered
Figure BDA0002281362590000053
The overall emotion index Q is given in detail belowbad、Qave、QgoodThe calculating step of (2):
step 1: for the jth microblog, the forwarding amount is assumed to be rjAnd the evaluation quantity is cjThe amount of praise is zjDefining a calculation formula of the 'hot degree' of the microblog as follows:
Figure BDA0002281362590000061
considering that the praise amount is larger in value, and is about 5-10 times of the comment amount and the forwarding amount in general, the praise amount and the forwarding amount are given a penalty of 0.2 time, so that the praise amount, the praise amount and the forwarding amount are almost in the same order range.
Step 2: if the ith large V publishes the micro blogs related to the stock market in a period of timeiAnd if so, the three emotions transmitted by the large V through the published microblog in the period are respectively as follows:
Figure BDA0002281362590000062
step 3: noting that the number of people concerned by the ith big V in the period is focusiThe number of vermicelli is fansiThe total number of microblog sending is weiboi，c1、c2Is a given constant. Considering the phenomena of 'mutual attention' and 'tabao' in the microblog, the influence of large V is evaluated "
Figure BDA0002281362590000068
In time, a certain punishment is given to the number of people concerned and the total number of microblog sending, and the specific definition is as follows:
Figure BDA0002281362590000063
Figure BDA0002281362590000064
Fansi＝fansi
then
Figure BDA0002281362590000069
Step 4: the overall emotion indexes of n large V on the microblog platform are as follows:
negative emotion index Qbad：
Figure BDA0002281362590000065
Non-emotional index Qave：
Figure BDA0002281362590000066
Positive emotion index Qgood：
Figure BDA0002281362590000067
By using the index calculation method, the emotion trend exploration is carried out on 96 large V on the microblog platform between the date 1 and 15 in 2017 and the date 2 and 15 in 2017, a trend graph is drawn and is shown in fig. 4, and the trend graph is compared with a large-plate (upper-syndrome index) market K line graph in fig. 5.
It can be seen that:
generally, the proportion of non-emotional indexes is large, and most published microblogs have no emotional tendency to some extent.
In general, the positive emotion index is higher than the negative emotion index, the positive emotion index and the negative emotion index normally fluctuate within a certain range, and when fluctuation is large, large change in stock market can exist.
As can be seen by comparing fig. 4 and 5, although the emotion assumes a more positive state in most cases, when the negative emotion is relatively high, the microblog emotional performance is more consistent with the trend of the large dish, which can also be exhibited by the fluctuation of emotion, just when the large dish (the upper witness index) assumes a certain degree of decline.
The microblog looks empty more, and can be used as a service project and provided for a client to make a decision reference. The customer can look at the front or the back. And finishing the final investment operation by combining the judgment of the client.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principles of the present invention are intended to be included within the scope of the present invention.

Claims (5)

1. A microblog emotion analysis method is characterized by comprising the following steps:
(1) crawling microblog data and calculating a microblog emotion value;
(2) performing label classification on microblogs related to stock market;
(3) performing category prediction on the obtained new microblog data by using a random forest model of Smote;
(4) the sentiment classification of each microblog by using a random forest algorithm based on Smote is as follows:
(41) for the jth microblog, the forwarding amount is assumed to be rjAnd the evaluation quantity is cjThe amount of praise is zjDefining the popularity omega of the microblogjThe calculation formula is as follows:
Figure FDA0002281362580000011
(42) if the ith large V publishes the micro blogs related to the stock market in a period of timeiAnd if so, the three emotions transmitted by the large V through the published microblog in the period are respectively as follows:
Figure FDA0002281362580000012
Figure FDA0002281362580000013
Figure FDA0002281362580000014
the probability of three categories of negative emotion, no emotion and positive emotion is p-1j、p0j、p1j；
(43) Noting that the number of people concerned by the ith big V in the period is focusiThe number of vermicelli is fansiThe total number of microblog sending is weiboi，c1、c2For a given constant, the influence of large V is evaluated
Figure FDA0002281362580000015
When, the definition is as follows:
Figure FDA0002281362580000016
Figure FDA0002281362580000017
Fansi＝fansi
then
Figure FDA0002281362580000021
(44) The overall emotion indexes of n large V on the microblog platform are as follows:
negative emotion index Qbad：
Figure FDA0002281362580000022
Non-emotional index Qave：
Figure FDA0002281362580000023
Positive emotion index Qgood：
Figure FDA0002281362580000024
2. The microblog emotion analyzing method according to claim 1, wherein the step (1) includes the steps of:
(11) establishing an emotion dictionary, including a positive and negative dictionary, a degree word dictionary, a negative word dictionary and an expression word dictionary;
(12) calculating a microblog emotion value: weight S corresponding to the active negative emotion word S is assumediThe weight corresponding to the emoticon SS is SSiEach microblog is subjected to word segmentation, cleaning and the like to obtain m1Positive and negative emotion words, m2Different expression words are arranged, and the number of the ith expression word is ni，
If m (less than or equal to m) exists1) The positive negative emotion words are only preceded by the weight WiIf the degree word is modified, the emotional value O of the microblog iskComprises the following steps:
Figure FDA0002281362580000025
if m (less than or equal to m) exists1) The positive negative emotion words are only preceded by the weight Wi noIf the negative word is modified, the emotional value O of the microblog iskComprises the following steps:
Figure FDA0002281362580000026
if m (less than or equal to m) exists1) The positive negative emotion words are preceded by the existing weight WiIs modified by the term of degree, and has a weight of Wi noIf the negative word is modified, the emotional value O of the microblog iskComprises the following steps:
Figure FDA0002281362580000027
3. the microblog emotion analyzing method according to claim 2, wherein in the step (2), microblogs related to stock market are manually classified into three categories:
the first type is microblogs expressing positive emotions to the stock market, the emotion tag is set to be 1, the second type is microblogs expressing negative emotions to the stock market, the emotion tag is set to be-1, the third type is microblogs not expressing emotion viewpoints, and the emotion tag is set to be 0.
4. The microblog emotion analysis method according to claim 3, wherein a random forest algorithm based on Smote is selected to classify original samples, independent variables of training samples in a classifier are obtained based on the emotion value calculation method according to the weight degree of emotion analysis dictionary design, namely: and calculating scores for different types and different weights of words respectively.
5. The microblog emotion analysis method according to claim 4, wherein 12-dimensional attributes are extracted for each microblog according to the score, wherein the positive emotion is divided into 3 dimensions, the negative emotion is divided into 3 dimensions, the degree word is divided into 4 dimensions, the negative word is divided into 1 dimension, and the emotion value is 1 dimension.
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